用哈希技术加速基于结构的异常检测，效率更高。
Hashing for Structure-based Anomaly Detection
- 用局部敏感哈希避免高维距离计算，提升效率
- 在偏好空间中识别最孤立点，性能达当前最优
- 适合需要快速检测异常的大规模数据场景
我们关注识别一组数据中不符合低维流形所表示结构模式的样本。一种有效方法是将数据嵌入高维空间（称为偏好空间），使异常点表现为最孤立的点。本文采用局部敏感哈希（Locality Sensitive Hashing）避免在高维空间中显式计算距离，从而提升异常检测效率。具体而言，提出一种基于隔离的异常检测技术，可在偏好空间中实现最先进的性能，且计算成本更低。代码已公开于 https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection。
原文摘要 · Abstract (English)
We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sensitive Hashing to avoid explicit computation of distances in high dimensions and thus improve Anomaly Detection efficiency. Specifically, we present an isolation-based anomaly detection technique designed to work in the Preference Space which achieves state-of-the-art performance at a lower computational cost. Code is publicly available at https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection.
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